Optimizing future dark energy surveys for model selection goals

TL;DR

This paper presents a methodology to optimize future dark energy surveys for model selection, using Bayesian figures of merit and Monte Carlo methods, to improve the ability to distinguish between different dark energy models.

Contribution

It introduces a Bayesian model selection-based optimization approach for dark energy surveys, enhancing flexibility and interpretability over traditional figures of merit.

Findings

Optimal survey configurations can achieve high model selection performance at lower DETF FoM levels.

Bayes factor-based methods provide better insight into survey design effectiveness.

The approach is practical for surveys similar to SuMIRe PFS.

Abstract

We demonstrate a methodology for optimizing the ability of future dark energy surveys to answer model selection questions, such as `Is acceleration due to a cosmological constant or a dynamical dark energy model?'. Model selection Figures of Merit are defined, exploiting the Bayes factor, and surveys optimized over their design parameter space via a Monte Carlo method. As a specific example we apply our methods to generic multi-fibre baryon acoustic oscillation spectroscopic surveys, comparable to that proposed for SuMIRe PFS, and present implementations based on the Savage-Dickey Density Ratio that are both accurate and practical for use in optimization. It is shown that whilst the optimal surveys using model selection agree with those found using the Dark Energy Task Force (DETF) Figure of Merit, they provide better informed flexibility of survey configuration and an absolute scale for performance; for example, we find survey configurations with close to optimal model selection performance despite their corresponding DETF Figure of Merit being at only 50% of its maximum. This Bayes factor approach allows us to interpret the survey configurations that will be good enough for the task at hand, vital especially when wanting to add extra science goals and in dealing with time restrictions or multiple probes within the same project.